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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/capture_and_replay.html">Introduction</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="notebooks.html">Legacy notebooks</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/dynamo.html">torch_tensorrt.dynamo</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
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</ul>
<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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  <section id="compiling-llm-models-from-huggingface">
<span id="compile-hf-models"></span><h1>Compiling LLM models from Huggingface<a class="headerlink" href="#compiling-llm-models-from-huggingface" title="Permalink to this heading">¶</a></h1>
<p>This tutorial walks you through how to compile LLM models from Huggingface using Torch-TensorRT. We also introduce KV caching in Torch-TensorRT which can greatly improve the performance of LLM inference.
The code is available in the <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/main/tools/llm">tools/llm</a> directory. We use the <code class="docutils literal notranslate"><span class="pre">run_llm.py</span></code> script to compile the model, generate outputs, and measure the performance.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This is an <strong>experimental release</strong> and APIs may change in future versions.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The compilation scripts and tutorials for Llama-2-7b-chat-hf and gpt2 models have been consolidated into the unified <code class="docutils literal notranslate"><span class="pre">run_llm.py</span></code> script located in the <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/main/tools/llm">tools/llm</a> directory.</p>
</div>
<section id="overview-of-tools-llm-directory">
<h2>Overview of tools/llm Directory<a class="headerlink" href="#overview-of-tools-llm-directory" title="Permalink to this heading">¶</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">tools/llm</span></code> directory provides the following tools to compile LLM models from Huggingface:</p>
<ul class="simple">
<li><p><strong>run_llm.py</strong>: Main entry point for model compilation, generating outputs, and benchmarking</p></li>
<li><p><strong>run_vlm.py</strong>: Entry point for compiling and benchmarking Visual Language Models (VLMs)</p></li>
<li><p><strong>Static Cache Utilities</strong>: <code class="docutils literal notranslate"><span class="pre">static_cache_v1.py</span></code> and <code class="docutils literal notranslate"><span class="pre">static_cache_v2.py</span></code> for KV cache optimization</p></li>
<li><p><strong>SDPA Attention</strong>: <code class="docutils literal notranslate"><span class="pre">sdpa_converter.py</span></code> and <code class="docutils literal notranslate"><span class="pre">register_sdpa.py</span></code> for registering scaled dot-product attention converter and lowering pass.</p></li>
<li><p><strong>Testing Components</strong>: Model-specific test files for validation</p></li>
<li><p><strong>Utility Functions</strong>: <code class="docutils literal notranslate"><span class="pre">utils.py</span></code> and <code class="docutils literal notranslate"><span class="pre">cache_utils.py</span></code> for common operations</p></li>
</ul>
</section>
<section id="supported-models">
<h2>Supported Models<a class="headerlink" href="#supported-models" title="Permalink to this heading">¶</a></h2>
<p>We have officially verified support for the following LLM families:</p>
<table class="colwidths-given docutils align-default">
<colgroup>
<col style="width: 20%" />
<col style="width: 40%" />
<col style="width: 20%" />
<col style="width: 20%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Model Series</p></th>
<th class="head"><p>HuggingFace Model Card</p></th>
<th class="head"><p>Precision</p></th>
<th class="head"><p>KV Cache Support ?</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>GPT-2</p></td>
<td><p>gpt2</p></td>
<td><p>FP16, FP32</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-odd"><td><p>LLaMA 2</p></td>
<td><p>meta-llama/Llama-2-7b-chat-hf</p></td>
<td><p>FP16, FP32</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-even"><td><p>LLaMA 3.1</p></td>
<td><p>meta-llama/Llama-3.1-8B-Instruct</p></td>
<td><p>FP16, FP32</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-odd"><td><p>LLaMA 3.2</p></td>
<td><div class="line-block">
<div class="line">meta-llama/Llama-3.2-1B-Instruct</div>
<div class="line">meta-llama/Llama-3.2-3B-Instruct</div>
</div>
</td>
<td><p>FP16, FP32</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-even"><td><p>Qwen 2.5</p></td>
<td><div class="line-block">
<div class="line">Qwen/Qwen2.5-0.5B-Instruct</div>
<div class="line">Qwen/Qwen2.5-1.5B-Instruct</div>
<div class="line">Qwen/Qwen2.5-3B-Instruct</div>
<div class="line">Qwen/Qwen2.5-7B-Instruct</div>
</div>
</td>
<td><p>FP16, FP32</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-odd"><td><p>Gemma 3</p></td>
<td><div class="line-block">
<div class="line">google/gemma-3-1b-it</div>
</div>
</td>
<td><p>FP16, FP32</p></td>
<td><p>Yes</p></td>
</tr>
</tbody>
</table>
</section>
<section id="supported-vlm-models">
<h2>Supported VLM Models<a class="headerlink" href="#supported-vlm-models" title="Permalink to this heading">¶</a></h2>
<p>We have officially verified support for the following Visual Language Models (VLMs):</p>
<table class="colwidths-given docutils align-default">
<colgroup>
<col style="width: 17%" />
<col style="width: 33%" />
<col style="width: 17%" />
<col style="width: 17%" />
<col style="width: 17%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Model Series</p></th>
<th class="head"><p>HuggingFace Model Card</p></th>
<th class="head"><p>Precision</p></th>
<th class="head"><p>KV Cache Support ?</p></th>
<th class="head"><p>Component Support</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Qwen 2.5 VL</p></td>
<td><p>Qwen/Qwen2.5-VL-3B-Instruct</p></td>
<td><p>FP16, FP32</p></td>
<td><p>Yes (static_v1 only)</p></td>
<td><p>Language Model only (Image Encoder not supported)</p></td>
</tr>
<tr class="row-odd"><td><p>Eagle2</p></td>
<td><p>nvidia/Eagle2-2B</p></td>
<td><p>FP16, FP32</p></td>
<td><p>Yes (static_v1 only)</p></td>
<td><p>Language Model and Image Encoder both supported</p></td>
</tr>
</tbody>
</table>
</section>
<section id="getting-started-with-run-llm-py">
<h2>Getting Started with run_llm.py<a class="headerlink" href="#getting-started-with-run-llm-py" title="Permalink to this heading">¶</a></h2>
<p>The main entry point is <code class="docutils literal notranslate"><span class="pre">run_llm.py</span></code>, which provides a complete workflow for model compilation and benchmarking.</p>
<section id="basic-usage">
<h3>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to this heading">¶</a></h3>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--model<span class="w"> </span>meta-llama/Llama-3.2-1B-Instruct<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--prompt<span class="w"> </span><span class="s2">&quot;What is parallel programming?&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--precision<span class="w"> </span>FP16<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--num_tokens<span class="w"> </span><span class="m">128</span><span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--cache<span class="w"> </span>static_v2<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--benchmark
</pre></div>
</div>
</section>
<section id="key-arguments">
<h3>Key Arguments<a class="headerlink" href="#key-arguments" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">--model</span></code>: Name or path of the HuggingFace LLM</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--tokenizer</span></code>: (Optional) Tokenizer name; defaults to model name</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--prompt</span></code>: Input prompt for text generation</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--precision</span></code>: Precision mode (<code class="docutils literal notranslate"><span class="pre">FP16</span></code>, <code class="docutils literal notranslate"><span class="pre">FP32</span></code>)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--num_tokens</span></code>: Number of output tokens to generate</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--cache</span></code>: KV cache type (<code class="docutils literal notranslate"><span class="pre">static_v1</span></code>, <code class="docutils literal notranslate"><span class="pre">static_v2</span></code>, or empty for no KV caching)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--benchmark</span></code>: Enable benchmarking mode for performance comparison</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--enable_pytorch_run</span></code>: Also run and compare PyTorch baseline</p></li>
</ul>
</section>
<section id="other-usage-examples">
<h3>Other Usage Examples<a class="headerlink" href="#other-usage-examples" title="Permalink to this heading">¶</a></h3>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compare different models performance</span>
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>gpt2<span class="w"> </span>--benchmark<span class="w"> </span>--enable_pytorch_run
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>meta-llama/Llama-3.2-1B-Instruct<span class="w"> </span>--benchmark<span class="w"> </span>--enable_pytorch_run

<span class="c1"># Generate the outputs (disable benchmarking) by specifying the number of tokens to generate. Default = 128</span>
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>gpt2<span class="w"> </span>--prompt<span class="w"> </span><span class="s2">&quot;What is parallel programming?&quot;</span><span class="w"> </span>--num_tokens<span class="w"> </span><span class="m">128</span>
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>meta-llama/Llama-3.2-1B-Instruct<span class="w"> </span>--prompt<span class="w"> </span><span class="s2">&quot;What is parallel programming?&quot;</span><span class="w"> </span>--num_tokens<span class="w"> </span><span class="m">128</span>

<span class="c1"># Test different caching approaches</span>
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>meta-llama/Llama-3.2-1B-Instruct<span class="w"> </span>--cache<span class="w"> </span>static_v1
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>meta-llama/Llama-3.2-1B-Instruct<span class="w"> </span>--cache<span class="w"> </span>static_v2

<span class="c1"># Compare FP16 vs FP32 performance</span>
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>Qwen/Qwen2.5-1.5B-Instruct<span class="w"> </span>--precision<span class="w"> </span>FP16<span class="w"> </span>--benchmark
python<span class="w"> </span>tools/llm/run_llm.py<span class="w"> </span>--model<span class="w"> </span>Qwen/Qwen2.5-1.5B-Instruct<span class="w"> </span>--precision<span class="w"> </span>FP32<span class="w"> </span>--benchmark
</pre></div>
</div>
</section>
</section>
<section id="getting-started-with-run-vlm-py">
<h2>Getting Started with run_vlm.py<a class="headerlink" href="#getting-started-with-run-vlm-py" title="Permalink to this heading">¶</a></h2>
<p>For Visual Language Models (VLMs), use <code class="docutils literal notranslate"><span class="pre">run_vlm.py</span></code> to compile and benchmark models that process both text and images.</p>
<section id="id2">
<h3>Basic Usage<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h3>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>tools/llm/run_vlm.py<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--model<span class="w"> </span>Qwen/Qwen2.5-VL-3B-Instruct<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--precision<span class="w"> </span>FP16<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--num_tokens<span class="w"> </span><span class="m">128</span><span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--cache<span class="w"> </span>static_v1<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--enable_pytorch_run<span class="w"> </span><span class="se">\</span>
<span class="w">  </span>--benchmark
</pre></div>
</div>
</section>
<section id="id3">
<h3>Key Arguments<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">--model</span></code>: Name or path of the HuggingFace VLM</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--prompt</span></code>: Input prompt for generation</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--image_path</span></code>: (Optional) Path to input image file. If not provided, will use a sample image</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--precision</span></code>: Precision mode (<code class="docutils literal notranslate"><span class="pre">FP16</span></code>, <code class="docutils literal notranslate"><span class="pre">FP32</span></code>)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--num_tokens</span></code>: Number of output tokens to generate</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--cache</span></code>: KV cache type (<code class="docutils literal notranslate"><span class="pre">static_v1</span></code> or empty for no KV caching)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--benchmark</span></code>: Enable benchmarking mode</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--enable_pytorch_run</span></code>: Also run and compare PyTorch baseline</p></li>
</ul>
</section>
</section>
<section id="kv-caching-in-torch-tensorrt">
<h2>KV Caching in Torch-TensorRT<a class="headerlink" href="#kv-caching-in-torch-tensorrt" title="Permalink to this heading">¶</a></h2>
<p>We provide two versions of static KV caching: <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/main/tools/llm/static_cache_v1.py">static_cache_v1</a> and <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/main/tools/llm/static_cache_v2.py">static_cache_v2</a>.
In both implementations, we add static KV cache tensors as model inputs/outputs without storing them as external memory.
The length of KV cache = input sequence length + output sequence length (specified by <code class="docutils literal notranslate"><span class="pre">--num_tokens</span></code>). The number of heads and head dimension are determined by the model config.</p>
<section id="id4">
<h3>Static Cache v1<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h3>
<p>The <code class="docutils literal notranslate"><span class="pre">static_cache_v1.py</span></code> implements KV cache in the model graph as follows:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">StaticCacheV1Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key_cache</span><span class="p">,</span> <span class="n">value_cache</span><span class="p">,</span> <span class="n">start_idx</span><span class="p">,</span> <span class="n">end_idx</span><span class="p">,</span> <span class="n">is_causal</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="c1"># Concatenate new key/value pairs with existing cache</span>
        <span class="n">new_key_cache</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">key_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">start_idx</span><span class="p">,</span> <span class="p">:],</span> <span class="n">k</span><span class="p">,</span> <span class="n">key_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">end_idx</span><span class="p">:,</span> <span class="p">:]),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="n">new_value_cache</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">value_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">start_idx</span><span class="p">,</span> <span class="p">:],</span> <span class="n">v</span><span class="p">,</span> <span class="n">value_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">end_idx</span><span class="p">:,</span> <span class="p">:]),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

        <span class="c1"># Compute attention using the updated cache</span>
        <span class="n">attn_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">scaled_dot_product_attention</span><span class="p">(</span>
            <span class="n">q</span><span class="p">,</span>
            <span class="n">new_key_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">end_idx</span><span class="p">,</span> <span class="p">:],</span>
            <span class="n">new_value_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">end_idx</span><span class="p">,</span> <span class="p">:],</span>
            <span class="n">dropout_p</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
            <span class="n">is_causal</span><span class="o">=</span><span class="n">is_causal</span>
        <span class="p">)</span>

        <span class="k">return</span> <span class="n">attn_output</span><span class="p">,</span> <span class="n">new_key_cache</span><span class="p">,</span> <span class="n">new_value_cache</span>
</pre></div>
</div>
<p>In the above code, we concatenate the new key/value pairs with the existing cache and update it. To compute the attention, we use the updated cache and gather the corresponding keys/values from the cache up until and including the current token index.
The above code is actually implemented as a FX graph transformation pass. We register it as a Torch-TensorRT lowering pass using the decorator <code class="docutils literal notranslate"><span class="pre">&#64;_aten_lowering_pass</span></code> when we import the <code class="docutils literal notranslate"><span class="pre">static_cache_v1.py</span></code> module.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The <code class="docutils literal notranslate"><span class="pre">start_idx</span></code> and <code class="docutils literal notranslate"><span class="pre">end_idx</span></code> are the start and end indices of the current token in the cache. For prefill phase, <code class="docutils literal notranslate"><span class="pre">start_idx</span></code> is 0 and <code class="docutils literal notranslate"><span class="pre">end_idx</span></code> is the input sequence length.
For decode phase, <code class="docutils literal notranslate"><span class="pre">start_idx</span></code> begins at the input sequence length and <code class="docutils literal notranslate"><span class="pre">end_idx</span></code> equals <code class="docutils literal notranslate"><span class="pre">start_idx</span> <span class="pre">+</span> <span class="pre">1</span></code>. The <code class="docutils literal notranslate"><span class="pre">start_idx</span></code> is incremented by 1 until the end of the sequence or we reach the maximum number of tokens to generate.</p>
</div>
</section>
<section id="id5">
<h3>Static Cache v2<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h3>
<p>The <code class="docutils literal notranslate"><span class="pre">static_cache_v2.py</span></code> is similar to <code class="docutils literal notranslate"><span class="pre">static_cache_v1.py</span></code> but it uses less number of slice operations. It implements KV cache in the model graph as follows:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">StaticCacheV2Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key_cache</span><span class="p">,</span> <span class="n">value_cache</span><span class="p">,</span> <span class="n">start_idx</span><span class="p">,</span> <span class="n">end_idx</span><span class="p">,</span> <span class="n">is_causal</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="n">concat_keys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">key_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">start_idx</span><span class="p">,</span> <span class="p">:],</span> <span class="n">k</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="n">concat_values</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">value_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">start_idx</span><span class="p">,</span> <span class="p">:],</span> <span class="n">v</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="n">new_key_cache</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">concat_keys</span><span class="p">,</span> <span class="n">key_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">end_idx</span><span class="p">:,</span> <span class="p">:]),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="n">new_value_cache</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">concat_values</span><span class="p">,</span> <span class="n">value_cache</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">end_idx</span><span class="p">:,</span> <span class="p">:]),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="n">attn_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">scaled_dot_product_attention</span><span class="p">(</span>
              <span class="n">q</span><span class="p">,</span> <span class="n">concat_keys</span><span class="p">,</span> <span class="n">concat_values</span><span class="p">,</span> <span class="n">dropout_p</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">is_causal</span><span class="o">=</span><span class="n">is_causal</span>
        <span class="p">)</span>

        <span class="k">return</span> <span class="n">attn_output</span><span class="p">,</span> <span class="n">new_key_cache</span><span class="p">,</span> <span class="n">new_value_cache</span>
</pre></div>
</div>
<p>In the above code, we concatenate the existing key/value cache with current key/value of the token. We use this to directly compute the attention and update the key/value cache inserting the current key/value.
The above code is actually implemented as a FX graph transformation pass. We register it as a Torch-TensorRT lowering pass using the decorator <code class="docutils literal notranslate"><span class="pre">&#64;_aten_lowering_pass</span></code> when we import the <code class="docutils literal notranslate"><span class="pre">static_cache_v1.py</span></code> module.
The definitons of <code class="docutils literal notranslate"><span class="pre">start_idx</span></code> and <code class="docutils literal notranslate"><span class="pre">end_idx</span></code> are the same as <code class="docutils literal notranslate"><span class="pre">static_cache_v1.py</span></code>.</p>
<p>After the model is compiled with static KV cache, the input signature of the model is changed. The new input signature is <code class="docutils literal notranslate"><span class="pre">(input_ids,</span> <span class="pre">position_ids,</span> <span class="pre">key_cache_0,</span> <span class="pre">value_cache_0,</span> <span class="pre">...,</span> <span class="pre">start_idx,</span> <span class="pre">end_idx)</span></code>.
The number of key/value cache tensors is equal to the number of attention heads in the model. We can use the <code class="docutils literal notranslate"><span class="pre">generate_with_static_cache</span></code> function to generate the outputs.</p>
</section>
</section>
<section id="generating-outputs">
<h2>Generating Outputs<a class="headerlink" href="#generating-outputs" title="Permalink to this heading">¶</a></h2>
<p>We use custom <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/9241476a868af46169348ab730d18907365a66ee/tools/llm/utils.py#L112">generate</a> function to generate the outputs. This function performs standard autoregressive decoding without KV caching.
There is also a <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/9241476a868af46169348ab730d18907365a66ee/tools/llm/utils.py#L141">generate_with_static_cache</a> function that performs autoregressive decoding with KV caching.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">generate_with_static_cache</span></code> function takes care of preparing the inputs to the model compiled with static KV cache.
The model inputs are <code class="docutils literal notranslate"><span class="pre">input_ids</span></code>, <code class="docutils literal notranslate"><span class="pre">position_ids</span></code>, <code class="docutils literal notranslate"><span class="pre">key_cache_0</span></code>, <code class="docutils literal notranslate"><span class="pre">value_cache_0</span></code>, …., <code class="docutils literal notranslate"><span class="pre">start_idx</span></code>, <code class="docutils literal notranslate"><span class="pre">end_idx</span></code>.
We initialize the key/value cache tensors with zeros and for every token generated, the new key/value cache tensors are the outputs of the model.</p>
<section id="sdpa-converter-sdpa-converter-py">
<h3>SDPA Converter (sdpa_converter.py)<a class="headerlink" href="#sdpa-converter-sdpa-converter-py" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p>Converts scaled dot-product attention operation using TRT Python API.</p></li>
<li><p>Supports causal and standard self-attention.</p></li>
</ul>
</section>
<section id="sdpa-registration-register-sdpa-py">
<h3>SDPA Registration (register_sdpa.py)<a class="headerlink" href="#sdpa-registration-register-sdpa-py" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p>This is a Torch-TensorRT lowering pass that replaces variants of SDPA with <code class="docutils literal notranslate"><span class="pre">torch.nn.functional.scaled_dot_product_attention</span></code>.</p></li>
<li><p>Registers the SDPA converter which is used for converting <code class="docutils literal notranslate"><span class="pre">torch.nn.functional.scaled_dot_product_attention</span></code> operation.</p></li>
</ul>
</section>
</section>
<section id="limitations-and-known-issues">
<h2>Limitations and Known Issues<a class="headerlink" href="#limitations-and-known-issues" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>Sliding window attention (used in Gemma3 and Qwen 3 models) is not yet supported</p></li>
<li><p>Some model architectures (e.g. Phi-4) have issues with exporting the torch model.</p></li>
<li><p>For VLMs, Qwen2.5-VL image encoder compilation is not supported due to dynamic operations incompatible with torch.export.</p></li>
</ul>
<section id="requirements">
<h3>Requirements<a class="headerlink" href="#requirements" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p>Torch-TensorRT 2.8.0 or later</p></li>
<li><p>Transformers v4.52.3</p></li>
<li><p>For VLM models (run_vlm.py):
- <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">qwen-vl-utils</span></code> (for Qwen2.5-VL-3B-Instruct model)
- <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">flash-attn</span> <span class="pre">--no-build-isolation</span> <span class="pre">-v</span></code> (for Eagle2-2B model)</p></li>
</ul>
</section>
</section>
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